Oral health status, related behaviours and perceived stress in xerostomia, Sicca and Sjögren’s syndromes patients - a cross-sectional study

Data were extracted from two cross-sectional datasets; 2017–2019 Lithuanian National Oral Health Survey (LNOHS) and 2020–2022 dry mouth study. The dry mouth study selected adult subjects from five capital hospitals: a specialised dental care hospital, a rheumatological centre, and three hospitals providing long-term care for patients with chronic, non-acute medical conditions. The study inclusion criteria were self-reporting mouth dryness assessed by a question: How often do you have dry mouth? (responses: ‘often’ or ‘always’), and consenting to participate in the study.

Considering the mean differences in caries experience between dry mouth groups, the minimum sample size calculation employed the G-power calculator considering 95% power and α = 5%. This calculation showed that we needed to recruit a minimum of 126 participants. In the dry mouth study, we managed to recruit 127 cases, and additional 55 cases were selected from the Lithuanian National Oral Health Survey (LNOHS), which employed a stratified random sampling from five biggest Lithuanian cities and ten randomly selected periurban/rural areas, one in each of the Lithuanian counties (details published elsewhere) [8, 23].

In the LNOHS, subjects with mouth dryness were identified in the same way as in the dry mouth study. To support analyses, three dry mouth groups were created. The xerostomia group comprised individuals who experienced dry mouth ‘often’ or ‘always’ and did not have Sicca or Sjögren’s syndromes. The Sicca syndrome group included participants with hyposalivation indicated by ≤ 0.1 ml/min of saliva production over 15-minute period following the unstimulated whole sialometry test and Schirmer’s test over 5-minute period showing ≤ 5 mm in at least one eye, and excluding participants with a Sjögren’s syndrome diagnosis. The Sjögren’s syndrome group consisted of subjects with a confirmed diagnosis according to the 2016 ACR/EULAR criteria [24]. We found that a portion of participants with Sjögren’s syndrome also experienced Sicca symptoms, and decided to include these patients in the Sjögren’s syndrome group; otherwise, some patients with Sjögren’s syndrome would have needed to be excluded, compromising internal validity. The detailed procedures of unstimulated whole sialometry, Schirmer’s test, and diagnosing Sjögren’s syndrome were presented elsewhere [8]. The comparison groups included controls which were age and sex-matched, and the control subjects for each of the dry mouth groups were selected from the LNOHS dataset [8]. In both studies, all data were collected by one trained and calibrated examiner (IS). The intra-examiner reliability was assessed using the intra-class correlation coefficient (ICC) calculated by comparing duplicate recordings of 10 randomly selected patients, which were not included in the main study, with 2 weeks between repeated clinical examinations. The ICC values indicating intra-examiner agreement were as follows: for the total numbers of decayed surfaces (ICC = 1.00,) missing surfaces (ICC = 0.99,) filled surfaces (ICC = 1.00,) teeth with 4 + mm periodontal pockets (ICC = 0.95 and for the teeth with signs of erosive tooth wear (ICC = 0.95) [23, 25].

The World Health Organization’s Oral Health Questionnaire for Adults [26] was used to inquire about participants’ sociodemographic characteristics (sex, age, education in years) and several oral health-related behaviours. These behaviours were measured as follows: frequency of tooth brushing (once a day or less ‘0’, twice a day or more ‘1’); use of interdental floss or toothbrush (no ‘0’, yes ‘1’); use of the fluoride toothpaste (no ‘0’, yes ‘1’); last dental visit within last year (no ‘0’, yes ‘1’); frequency of sweet consumption (a few times a day ‘5’ and every day ‘4’ were referred to as the ‘high’ consumption of sweets in univariate analysis, those who ate sweets several times a week ‘3’ or once a week ‘2’ comprised the ‘moderate’ category, and those consuming several times a month ‘1’ or rarely/never ‘0’ were defined as a ‘low’ sweet consumption. In the binary logistic regression, the original coding was used.

The stress levels were measured by the Perceived Stress Scale (PSS-10), which underwent forward-backward translation from English to Lithuanian languages [27] and subsequent validation. The construct validity of the Lithuanian version of the Perceived Stress Scale was evaluated employing the Exploratory Factor Analysis (EFA) and setting the Eigen value > 1.0 as the threshold for the factor (dimension) extraction. According to the Lee 2012 review of the psychometric adequacy of the perceived stress scale, we expected either one or, alternatively, a 2-factor solution [28]. In addition, to get a better insight into the scale’s dimensions, we employed the Varimax rotation and acquired dimension-based item loadings. Our EFA produced a two-factor solution, each representing clear dimensions (here factors). The ‘Helplessness’ dimension included six items, and the ‘Self-efficacy’ dimension included four items, where the first dimension explained 38.1% and the second dimension explained 20.5% of the scale’s variance. The ‘Helplessness’ dimension included the following items (loadings): item 1 (loading = 0.744), item 2 (loading = 0.816), item 3 (loading = 0.783), item 6 (loading = 0.681), item 9 (loading = 0.681) and item 10 (loading = 0.828.) The ‘Self-efficacy’s dimension items (loadings) were as follows: item 4 (loading = 0.779), item 5 (loading = 0.752), item 7 (loading = 0.727) and item 8 (loading = 0.606.) Our scale’s validation-related findings were in accordance with several earlier studies performed in different countries and diverse samples, which most commonly reported a two-factor PSS-10 structure. Our two-factor/dimension solution explained 59% of the total variance in stress levels and this finding is also in accordance with previous studies. To sum up, our validation-related testing indicates that the Lithuanian PSS-10 scale version is a valid measure.

For the clinical assessment of oral conditions, a plane mouth mirror and a CPITN periodontal probe were used as advised by WHO. Dental status was evaluated at a surface level as total numbers of decayed (DS), missing (MS) and filled surfaces (FS). Subsequently, the total DMFS summed all components reflecting one’s overall caries experience [26]. In addition, the number of teeth with signs and levels of erosive tooth wear were also recorded following the WHO methodology [26]. The percentage of teeth with periodontal pockets of 4 + mm indicated the extent of periodontal pocketing.

Analyses were performed using IBM SPSS version 28.0 software (IBM, NY, US), while age- and sex-matched control participants for comparison groups were selected using R 4.1.0 (R Core Team, 2021) package. The mean and standard deviation (SD), as well as the median and inter-quartile ranges (IQR) were calculated for continuous variables. The Chi-square test was used for categorical, and independent sample t-test or Mann Whitney U test were used for continuous outcome variables. The patient groups and their controls were compared in regard to sociodemographic characteristics, oral health-related behaviours and self-perceived stress. The Mann-Whitney U test compared oral health status between patients with dry mouth-related conditions and their controls selected independently for each dry mouth condition. For each dry mouth condition, multivariable binary logistic regression models tested oral health-related behaviours as independent predictors while adjusting their effects for the socio-demographic characteristics. For the multivariable binary logistic regression models, we selected the oral health-related potential predictors based on the literature, and all behavioural predictors in spite of their bivariate significance were included in the multivariable models. The adjusted effects of predictors are presented as Odds Ratios (OR) with their 95% confidence intervals (CI).